In the field of quality and claim management, managers are alerted when there are sudden increases in particular words and/or phrases (e.g., keywords) in communications data (e.g., customer complaints or comments). Conventional systems, such as IBM OmniFind Analytics Edition® (OAE), process text data and analyze keywords according to relevance and frequency. The analysis is used to detect problems related to a specific topic. Conventional systems operate by time stamping incoming communications (e.g., documents) and extracting keywords from the documents. A text mining index is then created which allows a manager to calculate the number of documents comprising a keyword within a specific time period. When a rapid increase in keywords is detected over a period of time, managers are alerted to investigate the issue.
One drawback of this arrangement is that an increase in keywords over a period of time may not indicate an essential problem. For example, an increase in the frequency of a keyword in communications as related to an increase in the number of sales of a product identified by that keyword may not necessarily indicate a fault or a complaint. Therefore, managers are falsely alerted if keyword data is analyzed based solely on an increase in frequency of keywords.
At present, false alerts are prevented through manual analysis and correction of keyword data based on experience. In the above example, managers would generally correlate that the increase in keyword frequency was directly related to an increase in the number of sales and therefore, there is no cause for concern. However, there is no consistent or accurate means of optimizing the accuracy of keyword data.